diff --git a/python/ray/tune/ray_trial_executor.py b/python/ray/tune/ray_trial_executor.py
index 2fc7178da..f1ede6bb6 100644
--- a/python/ray/tune/ray_trial_executor.py
+++ b/python/ray/tune/ray_trial_executor.py
@@ -97,7 +97,7 @@ class RayTrialExecutor(TrialExecutor):
             trial.runner = existing_runner
             if not self.reset_trial(trial, trial.config, trial.experiment_tag):
                 raise AbortTrialExecution(
-                    "Trial runner reuse requires reset_trial() to be "
+                    "Trainable runner reuse requires reset_config() to be "
                     "implemented and return True.")
             return existing_runner
 
diff --git a/python/ray/tune/schedulers/pbt.py b/python/ray/tune/schedulers/pbt.py
index 31259c41d..2953c1327 100644
--- a/python/ray/tune/schedulers/pbt.py
+++ b/python/ray/tune/schedulers/pbt.py
@@ -109,7 +109,7 @@ class PopulationBasedTraining(FIFOScheduler):
     This Tune PBT implementation considers all trials added as part of the
     PBT population. If the number of trials exceeds the cluster capacity,
     they will be time-multiplexed as to balance training progress across the
-    population.
+    population. To run multiple trials, use `tune.run(num_samples=<int>)`.
 
     Args:
         time_attr (str): The training result attr to use for comparing time.
@@ -162,7 +162,7 @@ class PopulationBasedTraining(FIFOScheduler):
         >>>         # 10 -> 1 or 10 -> 100. Resampling will choose at random.
         >>>         "factor_2": [1, 10, 100, 1000, 10000],
         >>>     })
-        >>> run_experiments({...}, scheduler=pbt)
+        >>> tune.run({...}, num_samples=8, scheduler=pbt)
     """
 
     def __init__(self,